Quality Control of raw sequencing data

Raw sequencing data are preprocessed to generate clean data for downstream analysis.

Quality of raw sequencing data is checked and filtered to retain only high quality bases by performing adapter trimming, quality filtering and per-read quality pruning. Quality is interpreted as the probability of an incorrect base call or, equivalently, the base call accuracy. The quality score is logarithmically based, so a quality score of 10 reflects a base call accuracy of 90%, but a quality score of 20 reflects a base call accuracy of 99% and a quality score of 30 reflects a base call accuracy of 99.9%. These probability values are the results from the base calling algorithm and depend on how much signal was captured for the base incorporation.

Sequencing reads representing reads with quality score at least Q30 is above 90% is of very good quality. For a reasonably good sample source material, according to Illumina specifications, one could expect >75% reads with at least Q30 Phred quality.

Raw sequencing data is processed using fastp[2] software to remove poor quality bases (below Phred Quality 20) using the sliding window approach where in if the average quality of the bases drops below Q20, those bases are removed from the reads. After quality trimming, program checks for presence of any adapters in the reads and removes from the reads. Further, shorter reads which are <30bp length are also removed to retain only high quality sequencing reads for each sample in the analysis. In case of paired-end reads, both the sequencing reads which pass the QC criteria are considered for downstream analysis.

After QC processing, QC metrics such as Q30 reads and GC content can be used to assess the sequencing and sample quality across the samples.

Read Statistics

• Table 1: Sequence Quality Metrics overview. For each sample, the following QC metrics are provided:
  • Sample Name: name of the sample.

  • Total Raw Reads: the total number of raw sequencing reads generated for the sample.

  • Total HQ Reads: the total number of high quality reads after sequence cleaning and filtering.
  • HQ Bases (Q30): Percentage of high quality bases having at least phred quality 30.
  • GC Content: GC content in percentile of high quality sequencing reads.
  • Mean Read Length (bp): Average read length in bp of high quality sequencing reads.

  • HQ Reads %: High Quality Reads percentage.

Taxa Abundance

Read counts of input samples observed at various taxa levels (Phylum, Genus, and Species) are collected and normalized by using the rarefy function implemented in the vegan bioconductor package[3] to compare species richness from all samples in the analysis run. Rarefied read counts enable better comparisons of OTU profiles between samples with different sample sizes. Abundance measured by the percentage of OTU assigned reads from various taxonomic levels is determined and are used to generate heatmaps and bar plots at Phylum, Genus and Species levels. Heatmap and bar plots representing the taxonomic abundance at species level are provided below.

Figure 1: Heat map(s) showing the taxonomic abundance and their relation across the samples. Dendrograms determined by computing hierarchical clustering from the abundance levels shows the relationship between the species (left) and the samples (top). The abundance levels (number of reads associated with each taxa) are logarithmically transformed to base 2 for clarity. Taxa-level: Species

Figure 2: Bar plot(s) showing the taxonomic abundance across the samples. Taxa-level: Species

Species Diversity

A diversity index is a quantitative measure that reflects how many different types (such as species) are in a dataset, and simultaneously takes into account how evenly the basic entities (such as individuals) are distributed among those types. The value of a diversity index increases both when the number of species increases and when all species are present at nearly the same level. For a given number of species, the value of a diversity index is maximized when all species are equally abundant. The following diversity indices are computed using vegan[3] package in R. Simpson refers to Simpson diversity index and has values ranging from 0 to 1. Values near 1 are simple environments and smaller values are diverse environments. InvSimpson refers to inverse Simpson diversity and has values >0. A larger value means greater diversity. Shannon refers to Shannon diversity index and has values >0. A higher value means greater diversity. Alpha refers to Fischer’s model of predicting species richness by computing alpha diversity and has values >0. A larger value means greater diversity. Evenness refers to the distribution of individuals across species and is determined by Pielou’s measure of species evenness. The index tends to 0 as the evenness decreases in simple environments (species-poor communities). SpeciesNo refers to the absolute number of species found in each sample.

Figure 3: Various diversity indices computed based on the species counts found in each sample

Rarefaction Curves

Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of rarefaction curves. This curve is a plot of the total number of distinct species found as a function of the number of sequences sampled. Sampling curves generally rise very quickly at first and then level off towards an asymptote as fewer new species are found in each sample. These rarefaction curves are calculated from the table of species abundance. The curves represent the average number of different species found for subsamples of the complete dataset.

Figure 4: Rarefaction curve of annotated species richness

Resistome Profile Results

Antimicrobial resistant genes and the associated antibiotic classes detected in each metagenomic sample are summarized in the following table.

• Table 6: ARG profiling metrics per sample:

Supplementary Information : Detected Antiobiotics Appendix

• Table 7: Resistome profiling appendix: